Improvement of Environmental Quality and Satisfaction of Living in New
Neighbourhoods by Priority of Actions on the Basis of Residents’ Views
(Case Study, Kashan)
Manochehr
Tabibian
Professor of Urban and Regional Planning, Faculty of urban planning, University collage
of Fine Arts, University of Tehran, Tehran, Iran
author
Yaser
Mansouri
Master of Urban and Regional Planning, Faculty of urban planning, University collage of
Fine Arts, University of Tehran, Tehran, Iran
author
text
article
2014
per
IntroductionHistoric cities and neighbourhoods in Iran couldn’t adjust themselves to quick changes of the recent decades andhave lost their quality in many aspects. Regardless of different cultural, economic and social conditions, newneighbourhoods have been formed next to old areas of the cities. Although these new areas welcom ed newfunctions, they couldn’t provide suitable environments for their residents. This paper intends to promote theenvironmental quality and people satisfaction of living in neighborhood by recognizing and prioritizing the mainenvironmental quality factors which have effect on satisfaction of living in neighborhood. Ghotb-E-Ravandineighborhood where is developed in the last few decades was selected for this study. This neighborhood islocated adjacent to historical fabric of Kashan City, in center of Iran.Materials and MethodsThe research method is an evaluation of residents. To determine the required samples for the evaluation,Cochran technique was applied. The indicators were measured by questionnaire was distributed among 164residents or 321 housing units in Ghotb-E-Ravandi Neighbourhood. The actual samples were selected by thesystematic sampling method by mapping the housing units. Data obtained through the residents interviews wereentered into a microcomputer and then analyzed by using SPSS. Each indicator was measured by at least twoquestions with five different answer levels (very high, high, moderate, low, and very low). Finally arithmeticmean of each category of questions was obtainded and scores of each indicator was applied in factor analysistechnique.Results and DiscussionThe indicators are including physical qualities, social relationships, accessibility, place identity, vitality, safety,security and urban facilities. These indicators, mostly qualitative in nature, were then divided into several subindicators,based on area, city, and social and cultural contexts. To do this, the 32 indicators were selected fromother studies and literature reviews. Because of the large number of indicators, multiple linear regressionanalysis cannot run in one step. Therefore, the 32 indicators were summarized in 6 factors by using factoranalysis technique (Table 1). To find the relation between residential environment quality and satisfaction ofliving in neighborhood, multiple linear regression analyses were used.In this analysis, dependent variable is satisfaction from living in new neighbourhoods and independentvariables are the 6 factors which are extracted from factor analysis technique. By using multiple linear regressionin SPSS, results of the study is summarized in 6 different models. In the models, the sixth one includes morevariable and higher adjusted R square value (75.1%) compared with others. Such figure covers 75.1% of changesof the satisfaction from living in neighbourhood that includes highest percentage among models. Besides, theStd. errors of the estimate are less than other models. Confidence interval in all factors is 100% and DW1statistical test is 2.3 which are appropriate2 for this model (Table 2). Table 1. Extracted Environmental quality factors from factor analysis techniquecorrelatewith factorvariablescorrelatewith factorFactor VariablesPermeability 0.903 Quality of houses 0.637Quality of play grounds 0.867 easy and safe walking and biking 0.617Access to green space 0.906 Sense of safety and security 0.620Quality of green spaces 0.809 Design based on local climate 0.497Recognition of landmarks in 0.751neighborhoodVacant and abandoned areas 0.463F1: quality andexistence ofpublic spacesResidents relationship 0.566 interaction with city managers 0.507social interaction and participation in 0.677 residents’ responsibility 0.797public activitiesSense of belonging and attachment to 0.714 A place that gives dignity 0.678neighborhoodwell-connected with important 0.711parts of the cityEvokes memories in places 0.582F2:Identificationand socialrelationshipNeighborhood center and sense of 0.583 Mixed use 0.583central locationAesthetic aspects of the neighborhood 0.712 Access to daily services 0.595Safety and security traveling at 0.507nightadequate open spaces between buildings 0.538F3: Vitality inneighbourhoodspacesAbsence of environmental pollution 0.730 Absence of noise pollution 0.808Easy access to downtown 0.501F4: Absence ofenvironmentalpollutionF5: Legibility Tendency to live in neighborhood 0.896 Legibility 0.896No automobile dependence 0.759 Access to public transport 0.560Access to urban facilities 0.671F6: Access tourban facilitiesNote. Only structural coefficients above 0.40 are reported.Table 2. Comparison of 6 regression models to show the relations between environmental quality and satisfaction ofliving in neighbourhoodModel R R Square Adjusted R Square Std. Error of theEstimate Durbin-Watson1 .658a .433 .429 .6612 .788b .621 .617 .5413 .820c .673 .667 .5044 .847d .718 .711 .4705 .864e .746 .738 .4486 .872f .760 .751 .436 2.034All variables are positive and at the same direction that is shown by increasing in each factor value,satisfaction from living in neighbourhood will be increased. Beta coefficient indicates the importance of factorsin changing the satisfaction from living in neighbourhood. Thus, if Beta coefficient of one variable is more thananother, this may show that the variable has more impact on the residents’ judgement. Regression formulaincludes main phenomenon (Satisfaction from living in neighbourhood) as dependant variable. The factors asindependent variable are as follow:Y= 0.166X1 + 0.658X2 + 0.435X3 + 0.227X4 + 0.212X5 + 0.120X6Y: Satisfaction from living in new neighbourhoodX1: Quality and existence of public spacesX2: Identification and social relationshipX3: Vitality in neighbourhood spacesX4: Absence of environmental pollutionX5: LegibilityX6: Access to urban facilitiesIdentification and social relationship and Vitality in neighbourhood spaces have the most impact, in order, onthe satisfaction from living in new neighbourhoods. ConclusionsCorrelation of 75.1 percent between dependent and independent variables shows the impact of environmentalquality on satisfaction from living in neighbourhood. In the same way 75.1% of changes in dependent variablecould be explained through independent environmental qualities variables. Therefore, environmental quality hasdirect relationship with satisfaction of living in neighbourhood.For prioritization of indicators, Beta quotient which shows the proportion of each factor on the satisfactionwas used. Then, by multiplying the Beta quotient by the proportion of each indicator in their factor, the impact ofeach indicator was recognized in the satisfaction. In the next step, by multiplying this amount by invertedaverage of indicator grade, the arrangement of priority of indicators for promotion of satisfaction by living inneighbourhood can be achieved. At the end, for promotion of the satisfaction, some solutions was recommended.The main physical indicators that should be considered to promote the satisfaction are includingneighbourhood that is well-connected with important parts of the city, aesthetic aspects of the neighbourhood,mixed use, neighbourhood center and sense of central location. The main social indicators are residents’responsibility, social interaction and participation in public activities, and interaction with city managers.
Journal of Environmental Studies
دانشگاه تهران
1025-8620
39
v.
4
no.
2014
1
16
https://jes.ut.ac.ir/article_36458_bccfbe7e6754f3ead40391e5d7d9b7c8.pdf
dx.doi.org/10.22059/jes.2014.36458
Estimation on Recreational Value of Tuchal Mountainous Region Using
Hickman Two Stages Econometric Model
Houman
Liaghati
Asosiate professor, Department of Environmental Economics, Environmental Science
Research Institute, Shahid Beheshti University, Tehran, Iran
author
Afsaneh
Naeemifar
Assisstant professor, Department of Agricultural Economics, Shahr-e-Qods Branch,
Islamic Azad University, Tehran, Iran
author
Naghmeh
Mobarghei Dinan
Assisstant professor, Department of Environmental Economics, Environmental Science
Research Institute, Shahid Beheshti University, Tehran, Iran
author
text
article
2014
per
IntroductionMountainous region of Tuchal, with a height about 3962 meters above sea level, is located in north of Tehran,capital of Iran. With regards to population growth, and increasing urbanization, and air pollution issues, it isknown as one of the most important recreational area for health recovery and relaxation among people living inTehran. As these spaces are known as public goods with free access, most of the visitors are not informed fromits real utility and values. This usually decline efficient and optimum use of these spaces and causes theirdegradation.The economic values of natural recreational regions not only increase their conservation by users, but alsocreate more accurate information for decision makers to improve properly other natural regions. It can also beeffective in forecasting requirements, omitting deficiencies and developing tourism industry in the recreationalareas.Materials and methodsIn this study, recreational value of Tuchal region is estimated with contingent valuation method from 227respondents. The method is used in open-ended way. Two stages method of Hickman has been chosen torecognize factors affecting on decision in willingness to pay in first stage and other factors affecting on deal ofwillingness to pay in the second stage. Required data was gathered via questionnaires and personal interviewwith 227 visitors. After deletion of incomplete responses and protest zeros, 47% of respondents were willing topay entrance fee for recreational use of the region. Table 1 show estimation results of Tobit Model forwillingness to pay for recreational use of Tuchal region.Table 1. Estimation results of Tobit Model for willingness to pay for recreational use of TuchalTotalElasticityRealizedElasticityExpectiveElasticityStatistic tNormalizedcoefficientVariableAge (year) -0.09 -1.87 -0.51 -0.4 -0.91Education (year) 0.0021 0.51 0.083 0.06 0.1430.00008 5.21 1.1 1.76 86.2Respondent Income(IRR1000)-0.62 -3.11 -0.474 -0.696 -1.17Number of FamilyMembers0.17 0.54 0.37 0.4 0.77Home Type (Apartment=1villa=0)0.079 1.97 0.42 0.17 0.59Air pollution in workplace(percentage)-0.000009 -0.28 -0.097 -0.131 -0.228Geographical change ofwork place(hours)Fixed Coefficient -0.013 -0.55 - -R2= 0.71 Hence, in this study, recreational value of Tuchal is estimated with contingent valuation method. The elicitmethod used is open-ended. Two stages method of Hickman has been used to recognize factors affecting ondecisions for willingness to pay in first stage and other factors affecting on deal of willingness to pay in thesecond stage. Table 2 shows the estimation results of Probit Model on willingness to pay for recreational use ofTuchal.Table 2. The first stage: Estimation results of Probit Model for willingness to payfor recreational use of TuchalFinal EffectTotal weightedElasticityElasticityin averageVariable coefficient Statistic tSex 0.258 1.91 0.33 0.3 0.061Age (year) -1.01 -0.75 -0.24 -0.21 -0.003Education (year) 0.316 0.29 0.015 0.014 0.000116Number of Family Members -0.77 -2.53 -0.75 -0.74 -0.17Respondent Income (IRR1000) 0.00051 3.2 1.62 1.41 0.000210.63 2.03 0.19 0.16 0.24Home Type (Apartment=1villa=0)-0.0901 -1.85 -0.41 -0.39 -0.0005Geographical change of workplace(hours)-0.44 3.06 0.53 0.52 0.0029Air pollution in workplace(percentage)Fixed Coefficient 2.73 1.62 0.12 0.1Correct prediction percentage= 80.3%McFadden R2 = 0.57Log-Liklihhod Function=-81.003Log-Liklihhood (0)= 147.15After deletion of incomplete responses and protest zeros, 82% of respondents was willing to pay entrance feefor recreational use of the region. The average of WTP was calculated 0.6 US$ per visitor and recreational valueof Tuchal area was estimated about 6000 US$ per hectare, in summer 2012.Other results also showed thatincome and facilities of the region (meaningful in 1% level) are the most effective variable in dealing with WTP.Table 3 shows the estimation results of linear regression model for the amount of entrance fee for recreationaluse of Tuchal Region.Table 3. Second stage: Estimation results of linear regression model for the amount of entrance fee for recreationaluse of TuchalVariable coefficient Statistic tAge (year) -29.7 -0.15Education (year) 33.1 1.14Number of Family Members -1004.2 -2.76Respondent Income (IRR1000) 0.886 4.01Home Type (Apartment=1 villa=0) 768.3 0.83Geographical change of work place(hours) -2.44 -0.145Use of flowers and plants at home (percentage) 34.6 1.94Air pollution in workplace (percentage) 127 2.09Number of visits (yearly) -1190.2 -1.86Inverse Mill Ratio 57.24 3.68Fixed Coefficient 9250.2 5.28R2= 0.57D-W=2.05Results and discussionHousehold size, impressiveness of pollution in work or living places of visitors, kind of houses (apartment orvilla) are the next effective variables (meaningful in 5%) on deal for WTP, in order. Although, education level isimportant factor and it is significant in 20% level. The age and geographic variety of work place affect justdecision to WTP in the first stage and not deal of it.With regards to high recreational value estimated per hectare in the region, it is required to pay moreattention to conservation of natural recreational regions. This is more important in big cities such as Tehran where habitants suffer from many kinds of pollution and deficiency of green spaces. The average WTP was 9444Rls per visitor and recreational value of Tuchal area was estimated about 137888240 Rls per hectare, in summer2012.ConclusionWith regards to high recreational value estimated per hectare in this region, it is required to pay more attention toconservation of other natural recreational sites with more responsibility. This is more important in big cities suchas Tehran with high level of air pollutions and deficiency of green spaces per capita.
Journal of Environmental Studies
دانشگاه تهران
1025-8620
39
v.
4
no.
2014
17
28
https://jes.ut.ac.ir/article_36459_3ea0473fff39a64a31ea25be96502f16.pdf
dx.doi.org/10.22059/jes.2014.36459
The Impact of Economic Variables on Environmental Pollution with
Emphasis on Financial Development Index: Application of Generalized
Method of Moments
Hassan
Heidari
Associate Professor of Economics, Department of Economics, Urmia University
author
Asall
Sadeghpour
Post Graduate Student Department of Economics, Urmia University
author
text
article
2014
per
IntroductionNowadays, environmental pollution is one of the main challenges in the world. Therefore, in addition to thepolicies and measures within their borders, countries prefer international organizations in the field ofenvironmental issues. Previously it was thought that economic growth causes an increase in income and will leadto improved quality of life. However, the high growth rate of the world economy in the last few decades withreduced environmental quality puts the environmental pollution in the spotlight in the globe. In most studies inthe literature on the investigation of economic factors effects on environmental pollution, these factors havebeen limited to economic growth and energy consumption. This study investigates the impact ofmacroeconomic variables such as economic growth, energy consumption, environmental pollution and an indexof financial development on countries with different level of income (low, medium, high) during the period of1980-2010. We apply a dynamic panel data approach with Generalized Method of Moment (GMM) estimatemethodology. Recent empirical studies show that the relationship between environmental degradation andper capita income level is similar to the turn-down U (primary Kuznets curve). The message of Kuznetshypothesis is that economic growth is the cause of infection and its treatment. In recent years we have witnesseda backlash economy for changes in financial statement which emphasizes the important role of financial markets.A variety of ways to finance the economy is moving toward the gates. But, there is a dichotomy in this case.Degree of economic and financial development decreases the environmental degradation. The results of somestudies show that financial liberalization and the adoption of policies to financial openness and liberalization toattract higher levels of R&D might reduce the environmental degradation. In this study, however, we areinterested in checking what the effect of financial development index is on the environmental pollution.Material and MethodologyIn general format EKC hypothesis can be specified as follow:E= f (Y, Y2, Z) (1)Where, E is environmental deterioration emission, Y is income indicator and Z is other variables affecting theenvironment.Following the empirical literature, the standard log-linear functional specification of long-run relationshipamong per capita carbon emissions, per capita energy consumption, per capita real income, and the square of percapita real income can be expressed as follows:CO= 1 + 2 en + 3 yt + 4 yt2 + 5 fd + Ut (2)Where, co is the carbon dioxide emission (measured in metric kilo grams per capita), en is the energyconsumption (measured in kg of oil equivalent per capita), y is per capita real GDP, y2 is the square of per capitalreal GDP, fd is the financial development indicator (domestic credit to private sector as a percentage of GDP)and Ut is error term.Empirical resultsThe preliminary step in this analysis begins by investigating the unit root test of the variables using the Im ,Pesaran and Shin (IPS) unit root test. Table 1 summarizes the outcome of the IPS unit root tests on the naturallogarithms of the levels of the variables. Table 1. Unit Root Test ResultsWith InterceptCountries with low income Countries with average income Countries with high incomecoefficient probability coefficient probability coefficient probabilityVariableCO2 9.05 0.00 6.53 0.00 15.27 0.00En 4.94 0.00 7.74 0.00 9.95 0.00Fd 5.48 0.00 6.87 0.00 11.22 0.00gr 5.66 0.00 4.60 0.00 4.9 0.00Y2 5.41 0.00 6.86 0.00 7.19 0.00Intercept and trendVariable Countries with low income Countries with average income Countries with high incomecoefficient probability coefficient probability coefficient probabilityCO2 7.59 0.00 6.35 0.00 8.16 0.00En 7.56 0.00 5.02 0.00 8.44 0.00Fd 5.71 0.00 6.4 0.00 8.19 0.00gr 5.71 0.00 4.7 0.00 4.53 0.00Y2 8.56 0.00 4.62 0.00 8.43 0.00The IPS unit root test results reveal that all the variables under investigation are stationary.The empirical results and estimates for equation on per capita CO2 emission for three different income groupsof countries are presented in this section. First, we discuss the results for per capita CO2 emission, economicgrowth and financial development along with energy consumption control variables. Then we discuss the EKCor curvilinear relationship between economic growth and CO2 emission in three different income groups ofcountries.Table 2 presents estimation results of the model for a panel of three different income groups of countries(low, medium, and high).Table 2. Estimation resultsvariable Countries with low income Countries with average income Countries with high incomecoefficient probability coefficient probability coefficient ProbabilityC(Constant) 3.51 0.04 -0.007 0.18 -0.85 0.01CO2(-1) -0.344 0.00 -0.185 0.00 -0.01 0.61En 0.41 0.11 0.277 0.00 0.86 0.00Fd 0.10 0.08 0.036 0.14 -0.96 0.00growth -2.35 0.00 -0.81 0.00 -6.31 0.00Y2 11.6 0.00 4.6 0.00 -3.29 0.00Wald test 45.33 138.9 201.46Sargan test 115.5 138.2 124.3The results for three different income groups of countries show that all signs of the estimated parameters areconsistent with the theory. The Sargan and Wald tests results confirm the validity of the interpretation of theresults. Energy consumption has positive effects on environmental pollution in all three income groups.Financial development in low-income countries has a significant and positive effect on the level of air pollution,while for the middle-income countries this relationship is not significant. The coefficient of financialdevelopment in countries with high income has a negative and significant impact on environmental pollution.Economic growth has decreased environmental pollution in all three income groups. However, environmentalKuznets curve is only confirmed in the high-income countries.ConclusionThis paper has investigated the impact of economic variables on environmental pollution with an emphasis onfinancial development index. We have used panel data approach with GMM estimate method. Our results havedemonstrated that financial development in low-income countries increases environmental pollution. It can besaid that these countries represent the facilities granted to the private sector in production, regardless of theenvironmental impact. In countries with high per capita income, this index has a negative impact onenvironmental pollution. This shows that, the private sector uses of funds, with investments in environmentalprotection measures and do their products. Moreover, the results show an inverse U relationship between economic growth and environmental pollution only for the countries with high per capita income. According tothese results, we suggest that the civilized world needs to move towards a new approach of theeconomical environment: Take a holistic approach that need strengthening and support throughinterdisciplinary collaboration and interaction, too much emphasis is placed on natural resources andenvironmental multidisciplinary professionals and experts in economics and political elites. It is one of the mostnecessary accessories to ensure the sustainable development.
Journal of Environmental Studies
دانشگاه تهران
1025-8620
39
v.
4
no.
2014
29
44
https://jes.ut.ac.ir/article_36460_896fe031f8b5aa594a27d98e3aa5d0cd.pdf
dx.doi.org/10.22059/jes.2014.36460
Identifying Ecological Vulnerability of Protected Complex of Touran via the
Methods of Reciprocal Effects Matrix, AHP, and EA
Shahrzad
Faryadi
Associate. Prof. Faculty of Environment, University of Tehran, Tehran, Iran
author
Hossein
Sepehr
Graduate student of Environmental Management and Planning, University of Tehran, Tehran, Iran
author
Majid
Ramezani
Post graduate student of Environmental Management and Planning, University of Tehran, Tehran,
Iran
author
text
article
2014
per
IntroductionEcological vulnerability is a common term that can be used in different hierarchical levels (animate, population,community, ecosystem, and landscape). Ecological vulnerability evaluation has lots of applications inenvironmental sciences such as EIA, risk assessment and environmental monitoring. This represents theimportance of the evaluation. This paper aimed at assessing ecological vulnerability of the protected area ofTouran (in East of Iran) using a combination of three methods of overlay, i.e., reciprocal effects matrix, AHP,and EA.So far, a large number of researches have been published about these methods around the world and Iran, aswell. Some works in Iran are “Degradation Model” and Jabbarian's work which has innovations in objectifyingecological vulnerability assessment with reciprocal effects matrix approach. We can also point to zonation ofenvironmental vulnerable and sensitive areas in west of Fars Province with method of fuzzy logic approach andAHP.Different methods have been used around the world to assess ecological vulnerability. Some of thesemethods are FAHP and compound the methods of AHP and GIS and also Multiway Data Analysis (MDA) fordetecting relations between indicators, Reciprocal of Fractal Dimension (SPCA) and compounding ecosystemsensitivity and landscape pattern.More diverse indices have been used in the field of ecological vulnerability, so far. Some of these indices areecological Sensitivity (ES), Natural and Social Pressure (NSP), Ecological Recovery Capacity (ERC) and theothers related with landscape such as Reciprocal of Fractal Dimension (FD), Isolation (FI) and Fragmentation(FN). In this paper, indices of Ecological Sensitivity are used because these data are available in Iran.Materials & MethodsProtected complex of Touran is in southeast of Shahroud City, southwest of Sabzevar City and in the north ofgreat plain of Kavir in Semnan Province (from 55 to 57 E and from 34° 44' to 36° 22' latitude).For calculating ecological vulnerability, first of all the reciprocal effects m atrix must be prepared. In thismethod, a matrix of ecological factors in which if the points of an ecological factor effects on other factors theyare given figure of one and otherwise figure of zero. In the next steps, the summation of rows and columns andthe degree of importance of ecological factors is calculate based on following equation.nij j i1S ( X X )Where Sij is the degree of importance of ecological factor, Xi is the number of ones in the row of i and Xj is thenumber of ones in column of j. Then by comparing the degrees of importance of ecological factors, table of AHPof ecological layers was made for identifying the degree of preferences of layers. In fact, using reciprocal effectsmatrix, process of preferences turned into objective method in AHP.Extent Analysis Method used for calculating preferences degree of layers, the preferences for some of thelayers would be negative; so simple AHP was used for calculating preferences degree of ecological factors. TheExtent Analysis Method was used in FAHP for scaling classes of layers.In the Extent Analysis Method after supplying hierarchy decision tree, pairwise comparisons wasaccomplished; then, using Extent Analysis Method these qualities converted into quantitative values. Numbersused in this method is Triangular Fuzzy Numbers. In this method for each row of pairwise comparisons matrix, value of Sk which is Triangular Fuzzy Number, is calculated. njminjk kj ij S M M111 1(1)Where k is the number of rows and i and j are alternatives and indices, respectively. After calculating S k s,magnitude degrees of them are obtained into each other. Generally if M1 and M2 are two Triangular FuzzyNumbers, magnitude degree of M1 into M2 is calculated as:( ) ( )( ) 11 2 1 21 2 1 2V M M hgt M MotherwiseV M M ifM M (2)Magnitude degree of a Triangular Fuzzy Number into K Triangular Fuzzy Numbers is calculated by thisequation:V M M M V M M and and ( ,..., k ) ( ) ... 1 2 1 2 ( ) 1 k V M M (3)Also calculating weight of indices is obtained from pairwise comparisons matrix, thus:( ) min 1 W x ( i k ) V S S (4)After scaling classes of layers and calculating preferences degree of layers, the obtained values are applied inmaps by GIS and using weighted overlay. Ecological vulnerability map of the area was provided. Ecologicallayers used in this work are elevation and aspect with five classes and slope with eight classes gotten from 50meter DEM, climatology with one class supplied by revised De Martonne, land use with seven classes,vegetation density eith six classes, soil depth with five classes, erodibility of soil with five classes, water erosionwith three classes, and finally wind erosion also with five classes.Results and DiscussionThe most effective factor in ecological vulnerability that obtained through reciprocal effects matrix and AHPmethod (Fig. 1) was erodibility of soil.This factor affects extremely other factors such as soil depth, watererosion and wind erosion. The weight of this factor in AHP was obtained about 0.371. The climatology andelevation factors are lower than the erodibility of soil. They are with preference degrees of 0.161 and 0.137,respectively. In the end of the list both layers of soil depth and vegetation density are affected by other factors,with preferences degree of 0.16.Finally, the map of ecological vulnerability was obtained by weighted overlay of the layers and also byapplying scales of classes for each layer. It is remarkable that location of the areas in sensitive geological zones,zones of deep soils, arid and warm climate, and wind erosion zones is determinant in vulnerability degree ofthose areas. These layers are converted into raster format and then overlaid by their weights; finally the map ofecological vulnerability was obtained as a result raster layer. For better land management, the area was classifiedby natural breaks method (Fig. 2) into four classes of resistant, subsensitive, sensitive, and vulnerable. Fig. 2. The map of ecological vulnerability for protected area of TouranConclusionsAccording to vulnerability map of the area, western parts are more vulnerable relative to other areas.Furthermore, most of the areas are placed in class of sensitive. Therefore, the protected area must be recognizedin different managerial levels for more conservational acts.
Journal of Environmental Studies
دانشگاه تهران
1025-8620
39
v.
4
no.
2014
45
54
https://jes.ut.ac.ir/article_36461_12415d6e34716d7027173180bae337e6.pdf
dx.doi.org/10.22059/jes.2014.36461
Intensities of the Urban Heat Island of Tehran under the Influence of
Atmospheric Synoptic Patterns
Ghasem
Azizi
Associate Professor of Climatology, Faculty of Geography, University of Tehran, Tehran, Iran
author
Aliakbar
Shamsipour
Assistant Professor of Climatology, Faculty of Geography, University of Tehran, Tehran, Iran
author
Mojtaba
Mahdian mahforouzi
M.Sc. Student of Climatology, University of Tehran, Tehran, Iran
author
Morteza
Miri
Ph.D. Student of Climatology, University of Tehran, Tehran, Iran
author
text
article
2014
per
IntroductionThe Urban Heat Island is a phenomenon whereby cities become warmer than the surrounding suburbs. In otherwords, there is a temperature difference between the cities and their surrounding areas. Generally, the UHI effectis a result of excessive and unplanned growth of urbanization. The behavior of artificial urban texture in terms ofabsorption of short-wave and long-wave radiation, transpiration, releasing of anthropogenic heat, and blockingprevalent wind is significantly different from that of the rudimentary nature. Hence, the Bowen Ratio in the citiesalters and the sensible heat increases. Surface geometry, on the other hand, decreases wind speed in urbanregions that plays a significant role in formation of UHI. Since the energy balance inside a city is altered, UHIintensity may change. This means UHI intensity is not spatially and temporally similar in different cities. It mustalso be noticed that UHI formation in a city usually has diurnal or seasonal patterns which are mostly affected bysynoptic weather conditions. There are three main synoptic and local climatology parameters that affect UHIformation: Air Pressure Systems, Cloudiness, and Wind Speed. Under stationary high-pressure systemconditions temperature differences between urban and rural areas become large. UHI intensity is largest in calmair and cloudless sky conditions and tends to disappear in cloudy and windy weather. Generally, synopticpatterns can be divided into three major conditions as stable, unstable, and mediocre. Unstable conditions reducethe heat island intensity by making turbulences which mix the air. Stable conditions, on the other hand, increasethe heat island intensity as they are calm and without air movements. Mediocre conditions can play two rolesdepending on their characteristics and wind properties.The urban heat island can lead to urban temperatures being 2–5C higher than those in rural surroundings.Studies have shown the difference in temperature between urban and rural regions (UHI Intensity: TUR ) isrevealed in minimum temperatures rather than maximums. Henceforth, the Maximum UHI intensity shouldusually occur after sunsets in urban areas. Other impacts of the Urban Heat Island could be intensifying pollutantconcentration over urban areas, altering local wind patterns, increasing humidity, forming cloud and fog, andchanging the precipitation rate over a city.Material and MethodologyIn this study, the influence of synoptic weather conditions on the intensities of the urban heat island of Tehranwas analyzed. Tehran is the largest and the most populated city of Iran, with an approximate area of 750 Km2and a population of 8 Million during night time. The city lies almost in the middle of the Tehran Province (1882Km2 of area) in the southern side of the Alburz Mountain and is limited to the highlands in northern and easternparts. On the southern and western parts, it is connected to the flat plains of Varamin, Shahriar and Karaj.To investigate the effects of synoptic weather conditions on the intensities of the Urban Heat Island overTehran, after a literature reviews, 24 days were selected from the year 2006; two days of each month of the year,one day with the highest and the other with the lowest air pressure over the urban area. After the homogeneity ofthe data derived from the surface station with those of the midlevel atmosphere (850 HPa, 700 HPa, and 500HPa) was examined, the climatologic data (including temperature, air pressure, wind direction and wind speed)for each day were gathered from different data sources: 1- Iran meteorology Organization stations includingsynoptic and climatology stations, 2- Air quality measuring stations including Air Quality Control Company(AQCC) and Department of Environment (DOE) stations. The location and distribution of the stations is shown in Fig. 1. After data refinement, the measuring times were transformed from Greenwich Mean Time (GMT) tothe Local Time (LT) by adding 3:30 to the GMT.The difference between the average temperature of the urban district (TU) and the rural area (TR) wascalculated by MS Excel 2007 for collecting the UHI intensity during different seasons and months and for all 24days at each measurement hour. The midlevel atmospheric data were gathered from National Centers forEnvironmental Prediction/National Center for Atmospheric Research of the United States (NCEP/NCAR). Then,all patterns occurred at mentioned days were manually observed and investigated. Then, the data including geopotentialmeter height, sea level pressure, wind characteristics, and temperature were analyzed using 2.5*2.5geographical grids. At the final step, the 4 days were chosen to represent the influence of the synoptic conditionson the heat island intensities. Two days, with the absolute maximum and minimum of UHI intensity, and twodays representing the total weather conditions of wintertime and summertime heat island. Fig. 1. Spatial Distribution of weather stations in the study areaResults and discussionAccording to the literature reviews, it was expected that during cyclonic condition the intensity of the UHIwould be reduced and inverse condition would be happened in anti-cyclonic condition. Figure 2 represents thevariation of the heat island intensities in the study days. As it could be seen, the absolute maximum intensity (8.9Celsius degrees) has occurred in July while the absolute minimum intensity (1.1) has occurred in January. It canalso be seen that the difference between the maxima and minima of heat island intensities have seasonal changes.While the difference between maxima and minima is the least in cold period, it is the most in the warm period. Infact, in the summer the maxima intensities raises more than those of the minima making the difference biggerthan what it is in the winter. It should also be noticed that the behavior of the minima and maxima issignificantly simultaneous. The maxima and minima almost increase and decrease together. Even in the summerin which the difference is bigger, the maxima and minima are closely correlated. Fig. 2. The variation of maxima and minima intensities of the urban heat island of TehranIn order to investigate the influence of the synoptic weather conditions on the heat island intensity for all fourdays, as mentioned previously, the sea level pressure map,wind field and geo-potential height was calculated(Fig. 3 and 4). Fig. 3. The synoptic weather condition for the absolute maximum (29th of July: A and B) and minimum (4th ofJanuary: C and D) time. A and C: sea level pressure (contours) and surface temperature in Celsius (coloredspectrum); B and D: wind direction (vectors) and wind speed in m/s (colored spectrum). Fig. 4. These are representatives of cold period (A and B) and warm period (C and D). A and C: geo-potential heightof 500 Hpa (contours) and sea level pressure (colored spectrum) for the occurrence time of minimum intensity of heatisland; B and D: geo-potential height of 500 Hpa (contours) and sea level pressure (colored spectrum) for theoccurrence time of maximum intensity of heat island.ConclusionIn this study the influence of synoptic weather conditions on the intensities of the urban heat island of Tehranwas investigated. The results indicated that the intensity of summertime heat island is higher than that of thewintertime. Furthermore, the correlation between the minima and maxima of heat island intensities shows theinfluence of the synoptic weather patterns on heat island intensity. In the combined maps it was revealed that thecorrelation between the maximum and minimum times of heat island intensity is much more significant in thewarm period while there are some inconsistencies in cold period. The reason for this condition could be thedifferent patterns of the atmosphere of Iran. In summer, the edge of Azores' subtropical high pressure is locatedin the midlevel atmosphere of Iran while there are several thermal low pressure cells near the ground. Thiscauses daytime turbulences due to the high radiation income and calm weather when the radiation effect islessened. However, the condition is almost the opposite in the cold period. In cold period, while there is a coldhigh pressure condition near the ground, the midlevel atmosphere experiences a relatively active pattern. Due tothe passing of westerlies, many unstable synoptic systems pass through Iran's atmosphere. The instability andvariety of passing systems increases the wind speed by which the heat island intensity is reduced or undergonevariation. Henceforth, the difference between the low level and midlevel atmosphere is the main cause for thevariation of the intensities of the heat island of Tehran.
Journal of Environmental Studies
دانشگاه تهران
1025-8620
39
v.
4
no.
2014
55
66
https://jes.ut.ac.ir/article_36462_f2243db9f3f384c8d83aa68c8939d84f.pdf
dx.doi.org/10.22059/jes.2014.36462
Aplication of TOPSIS Model in Site Selection of Paper Recycling Centers
Using GIS; Case study: Fars Province
Ata
Ghaffari Gilandeh
Assistant professor in Geography department, University of Mohaghegh Ardabili, Ardabil, Iran
author
Hamid Hasan
Yazdani
Assistant professor in Geography department, University of Mohaghegh Ardabili, Ardabil, Iran
author
Abdolvahab
Gholemi
Assistant professor in Geography department, University of Mohaghegh Ardabili, Ardabil, Iran
author
Farshid
Kazemi
M.Sc., Geography Department, University of Mohaghegh Ardabili, Ardabil, Iran
author
text
article
2014
per
IntroductionToday, one of the most prominent health and environmental problems of cities is ascending trend of solidwastes. This must be managed and the institutionalization of phenomena such as recycling, composting, and soon from municipal solid waste must be taken into consideration. In Iran, there is no essential management incollection, disposal and recycling of over 40 thousand tons of the waste per day which approximately 70 percentof them can be recycled into compost with thousands of plastics, papers and cartons. Thus, the waste are buriedin the excess trend or scattered around cities. Usually based on the weight and physical composition of municipalsolid wastes, we can say that most components of municipal solid wastes after organic solid wastes and animportant part of municipal and industrial solid wastes in most parts of the world are paper wastes. Whileamount of recyclable paper waste generated in Iran is considerable, but only a small part of that is recycled andthe remaining is exerted as garbage. While, it is possible to recycle the most part of the paper waste for theproduction of paper with high quality. In the recent decades, with growing public awareness of the dangers ofuncontrolled harvesting of hardwood resources, increasing prices of raw materials for paper production,decreasing the forest area in some major areas of wood production and resistance of organizations andenvironmentalists against the dameges, there are increasing demands towards the recycling of consumed paper inorder to meet the needs for paper.There are various capacities in the converting machines for the paper recovery that provide the possibility fordeveloping paper recovery centers in the regains with medium or large areas. With these qualities, investing inthe creation of paper recovery units and consequently selecting appropriate locations for the deployment of theseunits is a critical step for municipal waste management system. Therefore, it is necessary to institutionalize usingof decision support systems, in process of site selection for paper recovery units.The process to determine the suitability of land for locating paper recovery centers requires consideration ofmultiple criteria. That makes it necessary to use multi-criteria analysis models and techniques as inevitablechoice. Thus, using multi-criteria models and techniques that are applied in conjunction with GIS capabilitiescan be considered as the outstanding aspects of decision support systems (DSS) in the decision process. Usingdecision rules, we can classify alternatives according to priority in the process of site selection. Accordingly, inthis paper, there are intentions to test the operational capabilities of TOPSIS model as one of the leadingtechniques in multi-criteria decision making. This is applied in the experimental field of site selection for paperrecovery centers in Fars Province.MethodologyThe data and tools used in this paper are maps and information that have been collected based on the need for thecriteria and the constraints that are applied to determine the desirability of lands in locating paper recoverycenters in Fars Province. In this study, softwares have been used to fit the needs in the phases of data entry, datastorage, data management, data processing, data analysis, and etc. These softwares are including Excel 2007, ArcGIS 9.3, ARC View 3.3, Kilimanjaro IDRISI, and ILWIS 3.3. The main steps in the process of this study relatedto the research methodology are:1. Providing criterion and constraint maps that are used in locating landfills which have led to defining of 10criteria and 8 constraints. 2. Valuation and standardization of criterion maps: the process of valuation and standardization was performedbased on value of membership in fuzzy set. Standardization was performed using the possibilities that existin the FUZZY function of IDRISI Kilimanjaro software.3. The method for weighting criterion maps: in this step, we have tried to determine criterion weights andcriterion significance coefficient by using CRITIC method.4. Operational use of multi-criteria decision rules: in this step, there is intention to test the operationalcapabilities of TOPSIS model as a prominent example of the multi-criteria analysis techniques in theexperimental field of the site selection for paper recovery centers in Fars ProvinceDiscussionIn this research, classified maps represent suitability of locations for paper recovery centers.The values areassigned by the accomplishment of operational procedures and guidelines that is obtained the process of usingTOPSIS method (Fig. 1). In the obtained map as the scoreof each pixel approaches to 1, this is indicatingfavorable conditions for that pixel to be selected as a paper recovery center. Taking to consideration theconstraints (Fig. 2), these pixels can be used proportionally to show the ability of that area for another landuse.Therefore, obtained maps can be used as guidance by the decision makers in selecting appropriate locations forpaper recovery centers. For further documentation of the validity of land use suitability map that has been acquired in the process ofusing TOPSIS, we have tried to investigate on the characteristic of one sample pixel that is selected as preferableand is located on the area with no constraints, dealt with defined criteria.ConclusionIn this paper by considering Fars Province as a case study, capabilities and operational mechanisms of TOPSISmodel has been tested in site selection of paper recovery centers. In this step, there was the goal to test theoperational capabilities of TOPSIS model as a prominent example of the multi-criteria analysis techniques in theexperimental field of the site selection for paper recovery centers in Fars Province. For further documentation ofthe validity of land use suitability map acquired in the process TOPSIS application, we have tried to investigatefurther on the conditions of one sample pixel that is selected as suitable pixel on the area with no constraints.Results of the investigation indicatethat the pixels that are selected as preferable pixels in the output map havethe optimal conditions in terms of defined criteria. For example, this pixel that has been selected as a preferablepixel is labeled with scores more than 230 in 9 criteria and is located in the acceptable condition in terms ofdegree of membership in fuzzy function. Therefore, this model can be used as a Decision Support System (DSS)in the modeling spatial arrangement of paper recovery centers.
Journal of Environmental Studies
دانشگاه تهران
1025-8620
39
v.
4
no.
2014
67
88
https://jes.ut.ac.ir/article_36463_1c33e3d0b434bc1536d488f9f893c9a2.pdf
dx.doi.org/10.22059/jes.2014.36463
Investigation on Longitudinal Change of Karoon River Using
Linear Directional Mean (study area: Shoshtar to Arvandrod)
Jafar
Morshedi
Assistant Proffessor of Azad University, Shoshtar branch, Shoshtar, Iran
author
Seyed Kazem
Alavipanah
Proffessor, Faculty of Geography, University of Tehran, Tehran, Iran
author
Ebrahim
Moghimi
Proffessor, Faculty of Geography, University of Tehran, Tehran, Iran
author
text
article
2014
per
IntroductionRivers are always important features of the natural world. They perform vital function in agricultural,navigational, cultural, civilized andr ecreational associations. Mankind through his long history has tried tocontrol behavior of the rivers to change its effective elements for a stable situation.The first studies in this field date back to the “Aristotle” and “Archimedes” and the related applied studiesrefer to the applied matters about water and rivers in Chinese, Iranian, and Egyptian era. Those presented thegreat engineering services and management methods to the world.In the last centuries “Leonardo da Vinci”, “Guglielnini” and “Frisi” published the first findings about waterand rivers. The first classification about rivers based on relative degree of stability was carried out by Davis andafter that by miller & wolman, Schumm, Horton, Brierly & Fryirs. There are lots of researches and theoriesabout geomorphology of rivers and their changes in the publications of the scientists. This process has beenprogressed by the invention of photography in 1826 and airplane by Wright Brothers in 1900.The Existence of important agricultural structure like irrigation and drainage networks and the developmentof industrial and urban projects make clear the need of the society that are living in the sides of the Karoon river.The damages of geomorphologic changes, alteration of the land uses, wide change of the meander in rout of theriver and the role of these factors in natural, economical and societal change are the theories of survival of theintensity, type and amount of changes in Karoon River. This paper is studying the changes of Karoon Riverusing Linear Directional Mean. In Iran, in addition to the classical publications about geom orphologic processand landforms and river changes, there are many studies and researches that used different approaches andmethods in the field of morphological changes of rivers. This study has been done by using GIS and RStechniques. Therefore, many papers and studies have been published by Talvary, Khajesahoti & Bajestani,Aleyasin, Barjeste, Alavinejad, Ajdari & Rostami, Morshedi & Alavipanah. Most of these publications are aboutthe “Karoon River” in southwest of Iran.This paper tries to identify and determine some of the effective elements of the Karoon River morphologicalchanges by using GIS and RS. That may help government and managerial authorities of this region of Iran tohave a new view to the Karoon River behaviors.Location of the case study: The study area of this research is a lowland part of Karoon River Basin. That islocated in south west of Iran where continue from Shoshtar to the Arvandrod River in west of Khoramshahr. Thelength of this reach is about 364 kilometers, (the UTM projection system in the start and end points arex=298305 y=3553010 and x=227613 y= 3369477, respectively).The Karoon River area is 45231 squarekilometer that belongs to the Persian Gulf Watershed.Material and methodFluvial geomorphology studies are discussed in different ways and classified in two major groups, empirical andtheoretical methods. In the empirical group river changes is detected by using of fields study, historical maps,aerial photos and satellite images at different times and with analysis of the statistic data. But in the theoreticalmethods, researchers have used wide ranges of models and equations to prove their hypothesis about rivermorphology changes.In this study by using of the empirical methods, same as field works, air photos interpretation, GPS, compilation of special data sets, satellite images and historical maps, geomorphological changes of Karoon Riverhas been studied. By doing so, aerial photo, topographic map and satellite data of landsat, TM and ETM+ from1955 to 2007, have been used to extract the center line of Karoon River for a time interval about 52 years.Since fundamental requirement of the Landsat image is that they must be especially georeferenced, a precisegeometric correction is done. Then, based on the georefernced images supervised classification throughmaximum likelihood classification method is runned with null class. For this purpose, training site by fieldpoints and GIS vector layers has been used. This approach is just selected for extraction of the river channel andlakes borders. After that, the other geomorphologic features have been vectorized from the aerial photos and byvisual interpretation from images and geologic maps. Finally the geomorphologic features are classified by usingthe average length of channels and the rate of compass angel of channels relative to the north for driving the rateof changes.For having the correct rate of changes, the river pass is divided into thirteen reaches and the center line ofeach is drawn by the method. Then the mean lengths and direction of them has been obtained by using of arcGIS software. Finally, the changes of river course have been detected by combination of all of the findings fromsatellite data, GIS layers, and filed studies.In this study, GPS, ArcGIS, Arcview and Geomantic software have been applied for analysis andinterpretation of the data. By the way, mean directional linear of channel was drawn. This method is one of thenewest approaches to detect longitudinal and transverse changes of rivers. This model is a fast and easy way toget the best results in the studies about river changes.Measuring direction or orientationIn the method the trend of a set of line features is measured by calculating the average angle of the lines. Thestatistic used to calculate the trend is known as the directional mean. While the statistic itself is termed the"directional mean", it is used to measure either direction or orientation. Many linear features point to a direction–they have a beginning point and an end point. Such lines often represent the paths of objects that move, such ashurricanes. Other linear features such as fault lines have no start and end point. These features are said to have anorientation, but no direction. For example, a fault line might have a northwest–southeast orientation. You cancalculate the mean direction or mean orientation of a set of lines. In a GIS, every line is assigned a start and endpoint, and has a direction. The direction is set when the line feature is created by digitizing or by importing a listof coordinates.You can see the direction of each line by displaying it with an arrowhead symbol. When calculating the meandirection, it is necessary to ensure the directions of the lines are correct. When calculating the mean orientation,the direction of the lines can be ignored. The mean direction is calculated for features that move from a starting point to an end point, such as storms,while mean orientation is calculated for stationary features, such as fault lines (Fig. 1). There may be situationsthat it is needed to calculate the mean orientation of lines that represent movement. A wildlife biologist isinterested in where elk start and end during their seasonal migration. In such a case the mean direction of thepaths the elk take during each season must be calculated. However, the biologist would calculate the meanorientation if he or she were interested in the characteristics of the migration routes, in itself, to determine whatmakes a good route, rather than where the elk start and end. The biologist could calculate the mean orientationusing the elk paths in both directions (coming and going) and capture more about their movement. It is importantto remember that while most lines have many verticies between the starting point and the ending point; this tooluses only the start point and the end point to determine direction.ConclusionThe results of the first method show that Karoon River has general tendency from North-Northeast to South-Southwest. From 1955 to 2007, length of river has become longer. This process from 1955 to 1991 has beenincreased and after that from 1991 to 2007 this trend has been decreased. In general, most of changes haveoccurred in meandering reaches like reach number 2, and direct reaches show lower rate of changes similar tothe total rate of the river. Analysis of data indicated that it is possible to determine the rate of morphologicalchanges in reaches of the study by using of the mean lengths and direction (due to north) of the river courseduring the times of study. For doing so, it would be necessary to vectorize the center line of the river. On KaroonRiver the higher range of changes has been occurred from 1955 to 1991 in comparison with other periods.Construction of dams plays an effective role on the rate of morphologic changes by the control it exert upondischarge and sediment load. The results have revealed that the length of the first reach has been increased from1955 to 2007 in a tendency to have higher level of sinuosity.
Journal of Environmental Studies
دانشگاه تهران
1025-8620
39
v.
4
no.
2014
89
104
https://jes.ut.ac.ir/article_36464_66b4bf5f375b7fc79c4363e8cfc51343.pdf
dx.doi.org/10.22059/jes.2014.36464
Health, Safety, and Environmental Risk Assessment for Hydrocracker Unit of
Bandar Abbas in Refinement of Oil Company by EFMEA Method
Masoomeh
Bandarja
Master of Environmental Management, Islamic Azad University Bandar Abbas Branch
author
Seyed Ali
Jozi
Associate Professor of Environment, College of Engineering, Islamic Azad University
North Tehran Branch
author
text
article
2014
per
IntroductionOrganizations try to have a safe and healthy work environment without any pollution and damages. HealthSafety Environment (HSE) system is a tool for improving health, safety, and environmental conditions in allindustrial and non-industrial development programs. The system uses all human and financial resources toprovide people with a safe environment without any risk (Farshad et al., 2006). Hydrocracker unit in refinementof Oil Company of Bandar Abbas is an important refinement unit. Its activities may cause many environmentaland hazardous problems. The purpose of this study is to assess the situation of health, safety, and environment byEnvironmental Failure Mood and Effect Analysis (EFMEA) to minimize the negative effects and provide a riskmanagement program. Failure Mood Effect Analysis emerged for assessment of safety in systems is used todetect any possible defects in systems and subsystems based on quantitative analyses. This was modified in someways into EFMEA as a qualitative method. The later is applied for production development with the purpose tocharacterize and prioritize environmental perspectives (Dahlstrom, 2006). In this study the method is used in acombination of two kinds of domestic and international forms to design the hydrocracker form. The perspectivesof the unit activities are identified normal and repairing times.By a literature review on HSE this can be revealed that FMEA played an effective role in specification andmeasurement of performance indices. The researches indicated that EFMEA, in addition to finding accidentalaspects, can reduce harmful environmental impacts (Jozi et al., 2006). After ISO9001 was effectively founded,the EFMEA and also FMEA were introduced as a method to detect possible environmental failures and evaluatethe related risks (Jennings, 2008).MethodologyIn this study we have attempted to evaluate the aspects of the failures in normal, abnormal, and emergencysituations with combination of two forms provided for Iran and international levels. EFMEA method isconducted by a variety of experts (Tingstrom, et al., 2006) and the entire unit, thus, is as statistical population forthis study. The method is carried out in the following stages: identification of processes, potential failures asperspectives, consequences of the potential failures as the effects, severity in two magnitudes of environmentaland health risks, situation including three states of normal, abnormal, and emergency, potential causes offailures, occurrence as the repetition of the failure by a cause, detection as precautious measures, ranking ofresources consumption, raw material and energy, ranking of intensity of the effects or the amount of resourceconsumption, ranking of probability of occurrence and detection for resource consumption, determination ofRisk Priority Number (RPN), recommended precautious measures based on RPN, and ranking of theperspectives in hydrocracker unit. Designed form of hydrocracker identifies environmental, safety, and healthperspectives. For the identification the present situation is first recognized in safety and health perspectives.The analysis has been in two different stages: first the RPN was calculated with degree of hazardous usingfrequency distribution of data. Second, number of classes was then calculated. For this method theenvironmental perspectives have been entered in EFMEA form and the RPN calculated based on severity,probability of occurrence, and detection. After the degree ofh azard is obtained, the perspectives are rankedbased on the RPN and those more than the hazard value are considered as critical activities. For the first stage theRPNs have been sorted descending. For hazard value has been calculated by frequency distribution that requiresnumber and the length of classes. Results and discussionsIn the unit 24 activities have been investigated in normal times and 6 activities in repairing times. Recognitionand ranking of the perspectives have been based on previous experiences of occurred events as well as objectiveobservations. Up to 291 perspectives have been identified and evaluated in life cycle of production,consumption, and waste removal. From these about 119 perspectives has RPN more than hazard value. The riskvalue has been determined at 113. Thus, the perspectives more than this value are considered as criticalactivities. Then, 10 percent of the prioritized RPNs are categorized in three groups of risks as very high, high,and moderate. Some recommended advices have been suggested for these groups. In the EFMEA forms thathave been designed in this research for this hydrocracker unit, the perspectives of safety and health risks havebeen identified and evaluated in addition to environmental perspectives. Therefore, this form improved thequantitative forms of Rezazadeh Niavarani (2004) and that of Lindahl (2000) and made it better for identificationand evaluation of the research requirements.ConclusionsFrom these results it can be concluded that the highest perspective has been the environmental risk in times ofsubstantial repairing with RPN of 343 for consumption. The most quantities of risks are for health and securitywith 68 risks relative to those of environment with 54. Therefore, the results and discussions indicate that thishydrocracker unit has a relatively safe, healthy and environmental control system. But, because of theperformance of the system in high pressure and high temperature condition, modification and control conditionsseems necessary for promoting the security measures. The innovation of this study is that it improved theprevious forms for a more competent application.
Journal of Environmental Studies
دانشگاه تهران
1025-8620
39
v.
4
no.
2014
105
124
https://jes.ut.ac.ir/article_36465_c03907b414a6110afa387daf47299246.pdf
dx.doi.org/10.22059/jes.2014.36465
GIS-Assisted Investigation on Dispersion of BTEX in Industrial Regions of
Zarghan, Iran
Mansooreh
Dehghani
Associate Professor at Environmental Health Department, Shiraz University of Medical
Sciences, Shiraz, Iran
author
Mohamad Mehdi
Taghizadeh
Associate Professor at Environmental Engineering Department, Islamic Azad University-
Estahban Branch, Estahban, Iran
author
Ebrahim
Rastgou
Instructor at GIS Department, Apadana University, Shiraz, Iran
author
text
article
2014
per
IntroductionBTEX is a group of polycyclic aromatic hydrocarbons including benzene, toluene, ethylbenzene andxylene. Benzene is released to the atmosphere by both natural and anthropogenic activities. Benzene is emittedto the atmosphere mainly through the petroleum and petrochemical industries. The chronic exposure tobenzene may cause damage to kidneys, liver, lungs, heart, and nerves and also degrade DNA. Benzeneis a group (I) carcinogen. Toluene is used in many industries as a solvent. The exposure to low-tomoderatelevels of toluene can cause dizziness, drowsiness, nausea and hearing loss. The exposure to highlevels of toluene can cause permanent brain and speech damage, unconsciousness and even death.Ethylbenzene is used in the petrochemical industries. It is also used in industries including gas, oil,solvents, pesticides and dyes. Short-term exposure to high level of ethylbenzene can cause symptomssuch as respiratory irritation and neurologic effects. The long-term exposure to ethylbenzene affectsthe blood, liver and kidney. It is classified as a possible human carcinogen (2B) by IARC. Xylene is anaromatic hydrocarbon which is usedw ith benzene and toluene as a catalytic reform er in extraction and oilrefineries. Xylene is a major component of BTEX and is used as a fuel reformer. The inhalation of xylene affectsthe nervous system.Active sampling needs an air sampling pump to actively collect the air through a filter. However, passivesampling does not require a pump and the gases in the air are collected by diffusion. Passive diffusive airsampling is simple with high precision method widely used to monitor large-scales air pollution. GeographicInformation System (GIS) is a powerful tool to assess the contribution levels of BTEX sources. Haddad et al.(2005) used passive sampling to measure BTEX around gas stations in Shiraz.Since the industrial area of Zarghan is affected by numerous air pollution sources, the rapid and precisemonitoring systems are absolutely essential to detect and quantify polluting sources. Therefore, the objectives ofthis study are to (i) determine the dispersion level of BTEX using passive diffusive air sampling and GIStechniques and (ii) assess the contribution level of generating sources of BTEX in the urban areas.Material &MethodsZarghan is located 25 Km northeast of Shiraz, nearby Shiraz -Tehran highway. The town is also surrounded bymany different air pollution sources. Industrial complexes are located about 10 Km from Zarghan. In addition,the mountain in the east side of the town blocked the air flow through the town.We used a diffusive sampler to adsorb BTEX in the air by a tube consisting of adsorbent material. BTEXentered into the adsorbent tube by molecular diffusion. The adsorbent samplers were installed at the elevation of3 to 4 meters from the ground for the period of 17 days (March 2012). After the adsorption period, the samplertubes were sealed and returned to the laboratory for further analysis. After collecti ng the specimens, they weresent to Pasam Company in Switzerland for determination of BTEX. The extraction was done by carbon disulfide(CS2). Ion chromatography was used to analyze BTEX.Since many sources of air pollution are located in the Zarghan residential area, the boundary conditionsampling points were selected by mesh. Due to the small size of the study area, 10 points were selected and onesample was collected at each point. An image of the coverage area was obtained by Google Earth software. Geographical coordinates of 4 pointsof suitable dispersion were determined by the software and used in an Excel file as the ground reference. UsingArcGIS techniqus, the image was processed as the georeferenced map and the result saved in TIFF format with apixel size of 5 m. The data obtained from sampling of BTEX were interpolated using the passive samplingmethod with different methods such as Inverse Weighted Distance to the exponent of 2 (IWD)2, natural nearestneighbor. File Format (TIFF) is a raster image format with a pixel size of 5 m for each specimen (with at least 10sample points). All interpolated layersw ere then extracted by the border of sam ple point’s area to performinterpolation for all layers in the same extent. The geographic coverage area of BTEX concentration has beenstudied using different methods of Nearest Neighbor (NN), Inverse Distance Weighted (IWD), and Kringing.BTEX pollution maps were prepared using passive sample interpolation.Discussion and ConclusionThe ambient air quality guidelines for the annual concentrations of benzene in 2005 and 2010 are 10 gm-3 and3.6 gm-3, respectively. In most of the sampling stations, benzene concentration was in the standard limit (2.3gm-3 to 4.8 gm-3), but the concentrations in the following four stations were considerably high.1. Shiraz oil refinery sampling station with benzene concentration of 21.5 gm-32. Central old square with benzene concentration of 7.2 gm-33. Dudej with benzene concentration of 5 gm-3Data showed that Shiraz oil refinery sampling station with the maximum toluene concentration of 30 gm-3 ismuch lower than the 24-hour EPA standard limit. In the rest of the sampling stations, toluene concentrationswere lower. The average concentrations of toluene in residential areas of Canada were ranged from 11.5to 34.4gm-3 which is same as the range of concentration observed in Zarghan. The current study showedthat the concentration of xylene and ethylbenzene in all of the stations were much lower than the harmful levels.The odor of xylene can be recognized in the air at the concentration of 0.008 ppm. Therefore, during themaximum hourly pollution (inversion condition), the concentration of xylene is twice the average andthose who live near the mountain areas are able to detect its odor.The GIS interpolation showed that Shiraz oil refinery is the most important sources of benzene dispersion inthe study area. The relatively high concentration of benzene (21.5 gm-3) is dispersed in an area where isconfined in a radius of 1.5 Km from the refinery. Fortunately, Zarghan residential areas are not located withinthe affected zone, but according to Kringing and IDW interpolation, the concentration of benzene reached to theamount of 7.2 gm-3 in the old central square which is located in the center of the city. As seen in the Kringinginterpolation, Shiraz-Tehran highway is not a major source of benzene pollution, but it is expected to have a highconcentration of benzene near the mountain area. The four red stripes represents the high concentration oftoluene belong to Shiraz oil refinery, Zarghan, industrial Park and the highway. The nearest neighbor (NN)interpolation method showed the effects of Shiraz-Tehran highway more clearly. The pollution due toethylbenzene produced by the refinery is extended to a radius of 4.5 Km from the refinery. In addition, this isevident in the red zone of the industrial park and also in the old part of the city near the m ountain. Generally,data show exactly the same dispersion for all 3 isomers of xylenes. Comparing the various interpolation methodsused in the study, the IDW method shows the pronounced role of industrial zones while the Nearest Neighborinterpolation method indicates that the role of the highway is of a greater importance in Zarghan xylenespollution. The highest concentration of air pollutants expected to occur in the area surrounded by themountain. Therefore, the wind direction influences the general movements of the pollutants. Accordingto the GIS maps, the main source of air pollution produced by Shiraz oil refinery is significantlyconcentrated in the old central square station near the mountain area. Since there is no other air pollution sourcesin the area, the reason for increasing air pollution might be due to the trapping of contaminants near themountain area and blocked the air flow. The analysis of regression demonstrated that there is a linearrelationship between the concentration of pollutant at Old Central Square and the concentration in the oilrefinery with the regression coefficient of 0.98.
Journal of Environmental Studies
دانشگاه تهران
1025-8620
39
v.
4
no.
2014
125
136
https://jes.ut.ac.ir/article_36466_10a2934549597cb8e06ceab0c3bc19c8.pdf
dx.doi.org/10.22059/jes.2014.36466
The Concentration and Origin of Total Petroleum Hydrocarbons (TPHs) in
Coastal Sediments of the Kharg Island in the Persian Gulf
Hamideh Sadat
Mirvakili
Faculty of Environment, University of TehranMSc Graduate of Coastal Environment Engineering, Faculty of Environment, University of
Tehran, Tehran, Iran
author
Nasser
Hadjizadeh Zaker
Associate Professor of Environmental Engineering, Faculty of Environment, University of
Tehran, Tehran, Iran
author
text
article
2014
per
IntroductionPersian Gulf is a water body at the margin of Indian ocean which makes way to Oman Sea and Indian Oceanthrough the Strait of Hormuz with the minimum width of 56 km. Specific condition of the Persian Gulf such ashigh evaporation, high salinity, aqueous species diversity, fisheries value and especially oil sources of the area,which have extra importance in the world, have made it a sensitive and strategic area. Marine environment of thePersian Gulf suffers severely from oil pollution. Oil extraction and transportation together with the effluentdischrges from the coastal cities and oil refinneries are among the major sources of oil pollution in the Gulf.Kharg Island is a coral anticline in the Persian Gulf located within 28 km of the southern coasts of Iran. Thisisland has a length of approximately 8 km and a width of approximately 4 to 5 km. The coastal environment ofthe Kharg Island is rich with corals. The corals are the host to many types of local fishes of the Persian Gulf.Kharg Island contains the most important and also the biggest crude oil export terminal of Iran. More than 90%of Iranin oil exportation is conducted through the island. In addition, many other oil related activities includingstoring and filtering of the crude oil are performing in the island. Hosting extensive oil related activites duringthe past 50 years has subjected the marine and coastal environment of Kharg Island to huge amount of oildischarges from different sources icluding Oil Tanker accidents, leakages at the oil term inals, discharge of oilyeffluents from crude oil stores and oil refineres. However, surprisingly, there are only a few studies conductedon the oil pollution around the island. In this paper, the oil pollution and its origin in the near shore coastalsediments of the Kharg Island are examined and discussed. This is conducted by using the analysis of theconcentration of hydrocarbones in the sediment samples collected early 2012 from 11 points around the island.Materials and MethodsNear shore sediment samples were collected at 11 points around the Island. The stations were selected in a waythat they cover the whole parts of the island's coastline. The samples were also more concentrated near the oilterminals. All of the samples were taken near the coastline at knee deep water points in low tide condition. About200 to 300 grams of wet sediments were taken from the top 5 centimeter of surface sediment and was pouredinto boron silicate vial which was washed by washer material, hot water, acetone solution and distilled water andhad a lid of polyethylene. The collected samples were packed and protected for transferring to laboratory usingthe USEPA-sw-846 standard method.The standard method of American Association of Environmental Protection (USEPA-SW-846#3540C)named SOXHLET has been used for preparation of the samples and extraction of petroleum hydrocarbons fromthem. The samples were passed through a 63 micron sieve (<63) before the lab analysis. A gas chromatographydevice (GC-FID) Model VARRIAN was used to determine the concentration of Total Petroleum hydrocarbons(TPH) and aliphatic compositions in the sediment samples. In addition, the concentration of petroleum normalalkanes with different numbers of carbons (n-c 10, n-c 35) was measured.Result and DiscussionConcentrations of total petroleum hydrocarbons (TPH) in surface sedimentsTPH concentrations are represented by a diagram in surface sediment samples collected along the coastlines ofthe Kharg Island. The concentrations varied from a very low amount of 1 g/g to very high amount of 5624g/g. The highest TPH concentrations were observed at stations 2, 3 and 4, at 5624, 1660 and 4186 g/g,repectively. These stations were located on the eastern coast of the island near the T-shape quay, next to the major crude oil export terminal. The results indicate that the leakage from the loading activities have highlypolluted the coastal area around the T shape quay. Stations 9 and 10, where are located near Azarpod Jetty on thewest side of the island, have relatively high TPH concentration, 141 and 75 g/g, respectively. The oil pollutionin this area is resulted from the oil leakage in Azarbad oil terminal and also tanker incidents. The lowest amountof TPH concentration was observed in stations 1, 7, 8 and 11 up to <1, <1, 2 and 32 g/g, respectively. Station 1was located in the south of the T shape quay and is somehow close to this oil terminal. However, considering thedominant direction of the wind waves on the eastern coast, the sediments in the position of this station were notaffected from the oil discharges near the T shape quay. Staions 7 and 8 positioned on the northern part of theisland are far from the oil pollution sources near the oil terminals and were not affected from these sources. TPHconcentrations at stations 5 and 6 were 78 and 232 g/g, repectively. The concentrations in these stations aremuch less in comparison with the station 2, 3 and 4 adjacent to the T shape oil terminal. However, the T shapequay seems to be the source for the oil content of the sediments at these stations as well.Commendatore and Esteves (2007) dividedc oastal areas into three categories from the view point of oilhydrocarbon rate: low concentration (g/g < 10), low to average (10-100 g/g) and average to high (100-1000g/g). Readman et al, (2002) considered the sediments with concentration above 100 g/g as polluted. Tolosa etal, (2004 ) considered the sediments with concentration above 500 g/g as severe polluted and the ones withconcentration less than 10 g/g as non-polluted. Regarding to the above criteria the sediments on the easternpart of the Kharg Island adjacent to the T shape quay can be considered as highly polluted. The polluted areaextends to the north of the T shape quay along the east coast to the positions of stations 5 and 6. On the westcoast of the Island the polluted area are limited to the vicinity of the Azarpad Jetty. Apart from the abovementioned areas, the shallow water sediments around the rest of the Kharg Island in most of the west coast andin the north and south of the Island can be considered as not polluted.The origin of aliphatic hydrocarbons (AHC) in Kharg Island sedimentsHydrocarbons in sediments might be originated from fossil petroleum origin resulted from human activities ororiginated from biological activities of Algae, Planktons, Bacteria, marine animals and terrestrial vascular plants.In this part, the origin of hydrocarbons in studied region was determined through developing a set of presentindices and the position ofo bservance pollutants in each of the stations. In Kharg Island by regarding to oilactivities in this area, crude oil is considered as the first oil pollutant. Relative abundance of normal alkanes withdifferent numbers of carbons or predominance of the specified alkane in sediments can be the index of certaintypes of oil hydrocarbons presence in sediment samples. Therefore, the presence of 18-carbons normal alkane (n-C18) in sediment samples shows the oil origin of observed hydrocarbons (Clarke and Finely., 1973). Oddcarbons normal alkanes with low number of carbon atoms such as 17 carbon normal alkane (n-C17) is identifiedas the index of phytoplankton hydrocarbons presence (Tolosa et al., 2004). Normal alkanes with odd numbers ofcarbon 27, 29 and 31 (n-C31, n-C29 and n-C27) are introduced as the index of plant waxes presence with landorganic plant origin (Tulloch, 1976). N-C16 index, which is equal to n-alkanes/n-C16, is the number less than 15for polluted samples by fossil petroleum and the number more than 50 for polluted samples by biologicalhydrocarbons (Clarke and Finely., 1973). Carbon priority index (CPI) is defined such as equation (1) withBoehm and Requejo in1986:CPI= 2(C27+C29)/ C26 + (2C28) + C30 (1)The CPI index is about 1 for petroleum hydrocarbons, while it varies from 3 to 6 for vascular plants and fornon-polluted sediments by fossil oils (Colombo et al., 1989). The other index is the Odd/Even index, which isthe ratio of odd numbers of carbon atoms to even numbers. This ratio varies about 1 for petroleum, while forplant waxes; alkanes with odd chain are 8 to 10 times more than alkanes with even carbon chain.In some diagrams we show normal alkanes concentration profiles with different numbers of carbon atoms instations of the study area. As these profiles show, in stations numbers 2, 3 and 4, which are eastern dock ofisland (T shape quay) and in stations 8 and 9, which are on the proximity of western jetty (Azarpod jetty), 18carbons with normal alkane has the most rate in compositions and this shows the presence of oil hydrocarbons.At stations number 7 and 10, 17 carbons with normal alkane has the most rate and these rates show the presenceof phytoplankton hydrocarbons in these stations. At stations 1, 5, 6 and 11 by increasing the distance from oildocks placed on island, the rate of n-C17 concentration is more than n-C18 and this indicates that in thesestations present hydrocarbon in the sediment has biological origin. By regarding to insignificant concentration ofearth alkanes (n-C27, n-C29 and n-C31) in general profile of normal alkanes, there are no hydrocarbons withland vascular and organic plants in surface sediments of the study area. Because of the plant poverty in KhargIsland, lack of presence of terrestrial plants hydrocarbons is observed.The range of n-C16 index, in sediments of this region is 6.36 to 332.4. This rate in stations 2, 3, 4, 5, 8 and 9are less than 15 and typical of the polluted sediments of this region from petroleum. However, this rate is morethan 50 in other stations (stations 1, 6, 7, 10 and 11), this means the pollution of sediments in these regions is from the type of biological hydrocarbons. The other index is “CPI” which is between 0.75 and 5.7, and from itsresults in stations 2, 3, 4 and 10, we can find out that in all the point near the oil quays of Kharg Island, there areoil sources in coastal sediments. In other stations, the rates of these indices show that their hydrocarbons havebiological origins. Some stations including stations number 1, 5, 6, 7, 8 and 11 have not oil pollutions.Evaluation of observing hydrocarbons concentrations based on ratio of alkans with odd carbon numbers to evensshows that this ratio varies in a range from 0.8 to 7.36. These rates are equal to one in stations No. 2, 3, 4, 5, 8and 9.In other stations including 1, 6, 7, 10, 11 this quantity is more and there are high concentrations of oldnormal alkanes which suggests that there are biological sources with plant waxes for hydrocarbons. Therefore,general position of observed hydrocarbons in sediments of Kharg Island coastline suggests that there are fossiloil origins in the proximity of oil jetties in which high oil activities are performing and there are biological andnatural origins in other stations of the island.ConclusionThe pollution of the sediments in the studied area from the total petroleum hydrocarbons (TPH) was investigatedby oil hydrocarbons analysis in coastline sediments of Kharg Island and also by comparative analysis of theseconcentrations with the present guidelines and standards. The results of the studies based on the total petroleumhydrocarbons concentration indicate the high pollution in some stations and very low to average pollution inother stations from oil activities resources. At stations near to the eastern and western docks (particularly near Tshape quay), this rate is high and by getting away from oil terminals, this rate is reduceing.In northern andsouthern regions of island, this rate also reaches to the minimum. Using the relative indices, it has been shownthat the observed hydrocarbons in studied sediments have natural phytoplankton origin in farther points fromjetties; in addition to havingoil origin in most of the east and west stations and near the oil terminals.
Journal of Environmental Studies
دانشگاه تهران
1025-8620
39
v.
4
no.
2014
137
148
https://jes.ut.ac.ir/article_36467_ca6aaacba0c5f22907d8420893c86da8.pdf
dx.doi.org/10.22059/jes.2014.36467
Experimental Investigation of Arsenic Removal
by Using Fe Nano Particles in Batch Experiment
Mahdieh
Janbaz Fotemi
M.Sc. water resource management,University of Tehran, Karaj, Iran, 31587-77871.
author
Majid
Kholghi
Associate professor,Department of Irrigation and Drainage,University of Tehran, Karaj,
Iran, 31587-77871
author
Abdolhossein
Horfar
Associate professor,Department of Irrigation and Drainage,University of Tehran, Karaj,
Iran, 31587-77871
author
Davoud
Haghshenas
PhD, Department of Chemistry, Amirkabir University, Tehran, Iran.
author
text
article
2014
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IntroductionToxic and dangerous pollution in groundwater is enormous. This assenic pollution is concentrated more thanits permissible limits. It can be observed in different countries like India, Nepa l,Bangladesh, Pakistan, Taiwan,Thailand, Vietnam, Argentina, Brazil, Chili and Mexico. In some places in Iran like Hashtgerd and Kordestanthe arsenic pollution has been observed more than the permissible concentration. As the arsenic pollution isincreasing, many studies have been done to find different treatment options. Due to rapid removing of the As (V)and As (III) by using Iron Nano particles, this method have recently been considered useful.In this paper arsenic removal process was investigated by using nanoparticles. Based on batchexperiments, the influence of Zero-valent iron nanoparticles concentration, tem perature, pH, time, andarsenic initial concentration were observed in arsenic removal process. The results of this studyindicated that the Iron nanoparticles have high performance in arsenic pollution removal.Experimental MethodThe purpose of the current experimental study was to investigate the arsenic remediation process by using ironnanoparticles in batch experiment. Specific concentration of iron nanoparticles produced by PNF Corporationalong with the arsenite sodium salt was used. Specification of iron nanoparticle and arsenic has been shown intable 1 and 2.Table 1. Arsenic proportions in arsenic sodium saltElement Valance Percent % gr/ 1000ppmNa 23 0.18 0.31As 74.9 0.58 1O2 32 0.25 0.43NaAsO2 129.9 1 1.73Table 2. Specifications of iron nanoparticlesActual density(gr/Cm3)Bulk density(gr/Cm3)Specific surface(Cm2/gr)Purity(%)99.9 8-6 0.25-0.1 7.9Firstly, the polluted arsenic solutionw as put on shaker with a speed of around 250 rpm . Thenspecific concentration of iron nanoparticles were added to the solution and the test was begun. Thearsenic reducing process was investigated with sampling, conducted during the experiments. Sampleswere kept in dark glassetos prevent the arsenic oxidation. It should be m entioned that ironnanoparticles were separated from samples by filtering papers S&S with the size of 11m. Arsenicwas measured by coupling VGA and atomic adsorption machineries and the results were finallyanalyzed. In batch experiments, the effect of five parameters, i.e. time, pH, temperature, initial concentrationof arsenic and injection concentration of nanoparticles were investigated. Specifications of theexperiment have been showed in Table 3.Table 3. Specifications of experimentalTime(min)Iron nanoparticlesconcentration (gr/lit)Arsenic initialconcentration (ppm)SpecificationsExperimentalTime 0.5 1 2,5,10,15,30,60,90,120Iron nanoparticles concentration 0.5 0.5, 2 30,60,90,120Arsenic initial concentration 0.5, 5 1, 2 30,60,90,120Temperature 0.5 1 15,30,45,60pH 0.5 1 30,60,90,120In the first test, the solution containing 0.5 gr/lit arsenic and 1 gr/lit nanoparticles reacted after 1hour and the results showed that arsenic concentration reduced to below the allowable concentrationby using Fe nanoparticles in this time interval (Fig. 1 and 2). In pH test, alkaline, acidic and naturalenvironments were investigated and the result indicated that the reaction rate increased withdecreasing of pH. The results also indicated that pH increased during the test (Fig4) and this result wasone reason for decreasing reaction rate with time. For studying the temperature effect, two similar testswere done in 60C and 30C temperatures. In these tests, the reaction rate increased with increasingthe temperature. Initial arsenic concentration and injection iron concentration affected the reaction ratesignificantly. Consequently, the experiments were conducted by considering different concentration ofiron nanoparticles and arsenic thawt ere for arsenic concentration 5 and 0.5 ppm and for ironnanoparticles of 2 and o.5 gr/lit (Table 3). After that, the results indicated that the reaction rateincreased with increasing the arsenic or iron nanoparticles concentration (Fig. 3) because of theincrease in the contact between arsenic pollution and iron nanoparticles as reactive. Finally, the resultsrevealed that iron nanoparticles could effectively been used to eliminate the arsenic pollution. ConclusionNowadays, arsenic remediation as a toxic and widespread pollution is important in groundwater studies. One ofthe methods for the arsenic remediation is using the iron nano particles. This method involves lower costs withhigh performance and can be used for in-site pollutant remediation in aquifers. The result of this investigationindicated that the reaction between iron nanoparticles and arsenic lasts only about several minutes. Increase inthe temperature and decrease in pH reduced the reaction rate. Investigation of arsenic concentration com paredwith iron nanoparticles injection concentration revealed that the arsenic removal rate is increased by an increasein the ratio of nanoparticles to arsenic. For removing 500 ppb of arsenic concentration by using 1 gr/lit of ironnanoparticles, an exponential decreasing process was observed so that the arsenic concentration was reached toless than the arsenic permissible concentration during two hours. Finally it can be concluded that the capabilityof the Zero-valent Fe nanoparticles is a useful tool for removing the arsenic pollution in the groundwater.
Journal of Environmental Studies
دانشگاه تهران
1025-8620
39
v.
4
no.
2014
149
156
https://jes.ut.ac.ir/article_36468_21387ed6d36c4d6f00beb1372f367a53.pdf
dx.doi.org/10.22059/jes.2014.36468
Metals Pollution Assessment of Surface Sediment in Anzali Wetland and
Their Quality Zonation
Ahmad
Jamshidi Zanjani
PhD, Department of Hydraulics and Environment, School of Civil Engineering, Iran
University of Science and Technology, Tehran, Iran
author
Mohsen
Saeedi
Environmental Research Laboratory, Department of Hydraulics and Environment, School
of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
author
text
article
2014
per
IntroductionHeavy metals are originated from natural or anthropogenic sources. Mining activity, fuel combustion, urbandischarge, pesticides, agricultural and industrial activities are considered as the m ain anthropogenic sources ofthe metals. Generally, more than 90% of the toxic metals load in aquatic systems is bound on solid phase of theaquatic systems such as suspended matter and sediment. Thus, assessment of heavy metals pollution in aquaticsediment is a critical issue that has been studied by many researchers. Anzali International Wetland wasregistered in Ramsar Convention in 1975 (Ramsar site #40, Wetlands International Site Reference No.: 2IR005).It is located in Guilan Province (between 48°45' and 49°42'E longitude and 36°55' to 37°32'N) and covers 192Km2 that is considered as the main freshwater coastal wetlands in southern part of the Caspian Sea. Its catchmentarea with prevalent agricultural activities is about 3610 Km2. Moreover, presence of 41 major factories such aswood and paper mill companies, food industries, metal and related industries, plastics and tires, textile andelectrical machines are samples of anthropogenic sources in the study area. In the present study forty one surfacesediment samples in January 2011 were collected from Anzali International Wetland to assess metals pollutionstate and sediment quality zonation. Moreover, metals pollution assessments in the study area were conductedusing different existing indices, multivariate analysis approach and GIS tools.Materials and MethodsThe collected samples transferred to the laboratory in sealed plastic bags under 4C. After digestion usingHNO3/HCl/H2O2 according to U.S.EPA 3050B test method, total metal (Cu, Zn, Cr, Fe, Mn, Pb, Ni, Cd)contents were determined using Atomic Absorption Spectrometry (Bulck Scientific 210VGP). Moreover,sediment quality indices such as Modified degree of Contamination (mCd), ecological Risk Index (RI), andEnrichment Factor (EF) were applied to assess metal pollution state. In addition, multivariate statistical analysiswas conducted to determine probable sources of metals in the study area and interpret data. The investigationwas conducted by Principal Component Analysis (PCA).Results and DiscussionAccording to total metals content, the higher mean concentrations of all studied metals (except for Cr) werefound compared with those of earth's crust and mean world sediments. Moreover, the minimum content of Znand Pb are also higher than earth's crust content. Based on zonation maps about metals distribution, the sampleswith the minimum metals concentration were located in central part and Siahkeshim (Protected area) of the studyarea where there is no main sources of pollution. Moreover, the higher concentration of Cu, Zn, Cr, Pb, and Cdwere determined in eastern and northwestern part of the study area. These parts are affected by river dischargesthat pass through the more populated, industrialized and higher levels of agricultural activities areas. However,no distinguished distribution pattern of Fe, Mn, and Ni were detected. Overall, it could be concluded that centraland northern parts of the wetland that are less exposed to the sources of pollution demonstrated to have higherconcentrations of Fe, Mn and Ni. Moreover, results of applied aggregative indices such as ecological RI andmodified degree of contamination (mCd) revealed higher degree of metals pollution in the eastern part. Fig. 1depicts metals pollution state based on RI and mCd. In addition, the Cluster Analysis (CA) was applied to heavy metals concentrations in Anzali Wetland toverify probable metals relationship. The CA of variables based on Pearson Coefficient identified five clusters: 1.Cu-Zn; 2. Cr-Pb; 3. Cd; 4. Fe-Ni 5. Mn. The first cluster named A including metals (Cu, Zn, Cr, Pb, and Cd) thatexhibited higher degree of enrichment may indicate that they were originated from anthropogenic sources.However, it seems that Cd might be derived from different anthropogenic sources. The second cluster named Bwas made by Fe and Ni. It may be concluded that Ni and Fe are originated from same sources, while Mn incluster C has separate sources.Results of multivariate statistical analysis demonstrated three main principal components with theireigenvalues greater than 0.8. The cumulative variance of the components was about 81% of total variance. Thehigh positive loadings for Cu, Zn, Cr, and Pb, and moderate positive loadings of Fe, Ni, and Cd were extracted inthe first component with 46% of total variance. The loadingslevel of these two groups was not the same, so it could beconcluded that Fe, Ni, and Cd may be originated from thedifferent sources. In general, high loading values of Cu, Zn, Cr,and Pb in the first component may imply the anthropogenicsources of these metals. The positive loadings of Fe, Ni, and Mnwere found in second component with 24% of total variance.This fact may indicate natural sources of theses metals. Cd isdominant element in the third component with 11% of totalvariance. Results of PCA are depicted in Fig. 2.According to the results of PCA, it can be concluded that thefirst and the third factors are originated from anthropogenicsource like agricultural and industrial activities, and discharge ofurban wastewater and leaching from prevalent dumping waste inopen space that is considered as the main waste disposalmethods in the north of Iran.ConclusionIn the present study, metals concentrations in surface sediments of Anzali Wetland were determined. Manyprevalent and useful indices such as mCd, ecological RI, and EF were applied to assess metals pollution state inthe collected samples. Moreover, based on the results of applied indices and total metals content, sedimentquality zoning were performed using GIS software. Overall, it could be concluded that aggregative indices suchas mCd and RI could assess metals pollution state in surface sediments of the study area in an acceptable manner.According to the obtained results, eastern parts of the Anzali Wetland were more polluted than the other parts ofthe area. Moreover, concentration of all studied metals except Cr was higher than those in the earthcrust.Application of multivariate statistical analysis also revealed that Cu, Zn, Cr, Pb, and Cd may be originatedfrom anthropogenic sources and metals like Fe, Mn, and Ni might be derived from natural sources in the studyarea.
Journal of Environmental Studies
دانشگاه تهران
1025-8620
39
v.
4
no.
2014
157
170
https://jes.ut.ac.ir/article_36469_3a861b3504640e65820ca1c71b862861.pdf
dx.doi.org/10.22059/jes.2014.36469
Simulation of Thermal Stratification and Dissolved Oxygen Concentrations
Using Ce-Qual-W2 Model (Case Study: Shahid Rajaee Dam)
Pooneh
Saeidi
PhD Student, Graduate Faculty of Environment, University of Tehran, Tehran, Iran
author
Nasser
Mehrdadi
Professor of Graduate Faculty of Environment, University of Tehran, Tehran, Iran
author
Mojtaba
Ardestani
Assistant Professor of Graduate Faculty of Environment, University of Tehran, Tehran,
Iran
author
Akbar
Baghvand
Associate Professor of Graduate Faculty of Environment, University of Tehran, Tehran,
Iran
author
text
article
2014
per
IntroductionDue to shortage of fresh water resources, the quality of impounded water behind the dams becomemore important than how it was previously as a source of fresh water resource. Thermal regime anddissolved oxygen concentration are factors that affect the quality of water reservoirs.Many lakes show vertical stratification of their water masses, at least for some extended timeperiods. The atmosphere imposes a temperature signal on the lake surface. As a result, thermalstratification can be established during the warm season as a lake is sufficiently deep. On the contrary,during the cold period, surface coolingfo rces vertical circulation of water masses and removal ofgradients in water properties. However, the gradients of dissolved substances like dissolved oxygenmay be sustained for periods much longer than one annual cycle. In order to understand the annualcycle of temperature and dissolved oxygen in Shahid Rajaee Reservoir, Ce-Qual-W2 model was used.Study areaShahid Rajaee Reservoir Dam located over the Tajan River almost 40 km south of Sari, Mazandaran,Iran. Construction purposes of this dam is including water supply and regulation of for agriculturalactivities in Tajan lowland, potable water supply for the population within the plan area, industrialwater supply, power generation, flood control, and prevention of the damage by flooding. The damtype is double curvature concrete arch dam and its height is about 133.5 m. reservoir volume is about165 MCM and was constructed from 1987 until 1997.DiscussionsShahid Rajaee reservoir dam is simulated using a two-dimensional, laterally averaged, hydrodynamicand water-quality model, CE-QUAL-W2. Hydrodynamics, temperature, and dissolved oxygen aresimulated and then calibrated with observed data to verify accuracy. The input data used in this modelare the best available and are assumed to be accurate representations of meteorology, flow, and waterquality parameters.Meteorological data for the model include air and dew point temperature, wind speed, winddirection, and cloud cover observations and daily mean flow rates. These data are collected for aperiod from 2001 to 2011. Data for water quality parameters are taken from Mazandaran WaterCompany for the years from 2010 to 2011. When data are not available, statistical relationships wasapplied to supplement the water quality data.The hydrodynamic model built and calibrated for the years from 2001 to 2011. Then the model wasused to simulate the thermal regime and dissolved oxygen concentrations for the period with twoassumptions. The first assumption is continuation of current situation and the second is a 50% increasein water requirements. The bathymetric grid was generated using topographic maps in scale 1:100000. The water bodywas divided into 95 segments, and 45 layers. The segments have 50 meters length and all layers are 2meters thick. The accuracy of the bathymetry data was checked using storage-capacity curves. Thecurves show reservoir storage at different reservoir elevations. The com parison of the model volumeto the actual storage capacity is made to verify the accuracy of the model grid. Calibration data includetemperature and DO concentrations measured at several monitoring sites taken at depth intervals of 1to 15 meters from the water surface to the reservoir bottom.ConclusionsThe results indicate the thermal stratification in summer and vertical mixing in winter. This regime ispredicted for the years from 2010 to 2014 in Fig. 1. Based on These results Shahid Rajaee Reservoir is in branch of warm Monomictic lake. WarmMonomictic lakes are lakes that never freeze, and are thermally stratified throughout much of the year.The density difference between the warm surface water (the epilimnion) and the colder bottom water(the hypolimnion) prevents these lakes from mixing in summer. During winter the surface water coolto a temperature equal to the bottom water. Lacking significant thermal stratification, these lakes mixthoroughly each winter from top to bottom. Dissolved oxygen modelling results showed that itsconcentration at reservoir bottom is zero when thermal stratification dominates. Dissolved oxygenconcentration will be homogeneous at winter when thermal vertical mixing dominates. WinterAnaerobic conditions in the bottom of the reservoir are fading and the reservoir is homogeneous in thevertical direction. Change in dissolved oxygen concentration is also predicted for the years from 2010to 2014 in Fig. 2. The 50% increase in water requirement caused a decrease in water levels and water retention timein the reservoir. Besides this issue, 50% increase in duration of water requirement occurred in summerand the presence of anaerobic conditions decreased in the bottom of the reservoir.
Journal of Environmental Studies
دانشگاه تهران
1025-8620
39
v.
4
no.
2014
171
180
https://jes.ut.ac.ir/article_36470_3ef495c6f4003b72c2b6a3877d672076.pdf
dx.doi.org/10.22059/jes.2014.36470
Evaluation of Health Risk Assessment by Heavy Metals in the
Ambient Air of Tehran
Ali Reza
Noorpoor
Associate Professor of air pollution, Faculty of Environment, University of Tehran, Tehran,
Iran,
author
Arash
Sadri Jahanshahi
MSc. Student in air pollution, Faculty of Environment, University of Tehran, Tehran, Iran
author
text
article
2014
per
IntroductionAnnually, heavy metal pollution is increasing in the environment and this eventually causes serious hazards forhealth of human, animal and plant populations. Heavy metals with their harmful effects are the major pollutantsin big cities. Tehran is a big city and faced with this problem. Heavy metals such as arsenic, iron, zinc, lead,cadmium, chromium, copper, manganese and nickel exist in the air of Tehran. These polluteants are inhaled byinhabitants and cause serious problems for human body. Among streets, roads and highways of the city, EnqelabStreet is one of the busiest and particularly from Enqelab Square to Imam Hossein Square.In this study, the results of measuring heavy metals including arsenic, iron, zinc, lead, cadmium, chromium,copper, manganese and nickel in the air of the streetare presented with the health risk assessment frompermanent and temporary residents in thearea. Moreover, the risk of developing cancer and non-cancer diseasescaused by inhaling the polluted air with heavy metals was also estimated.MethodologySelecting Sampling PointsThe Enqelab Street connects Enqelab square to Imam Hossein Square. Considering that risk assessment is amethod based on residents' health, the main crossroads and squares are selected as the sampling points.Therefore, sampling was performed in 5 stations: Enqelab square, Valiasr Crossroads, Ferdowsi Square, PicheShemiran, and Imam Hossein Square.Sampling Method and Chemical AnalysisIn this phase, in order to determine the concentration of heavy metals (including arsenic, iron, lead, cadmium,chromium, copper, manganese and nickel), air samples were collected and analyzed in two different seasons (onFebruary 7th 2013 and May 22nd 2013) during 8 hours from 5 stations. The entire process was performedaccording to OSHA-125G standard method.Quality Control of the AnalysisIn order to determine the quality of analysis methods, precision and accuracy were tested. The precision is from3 to 17 percent and average percent recovery isv aried between 83 and 97 percent.This is placed within theacceptable range of US Environmental Protection Agency guidelines.Overview of Risk AssessmentIn this study, the average value of the air inhaled by one inhabitant in Tehran is measured so that by calculatingthe air pollutant concentrations, the amounts of heavy metals which are entered into his body are obtained. Forthis purpose, three groups of people are defined in terms of the type and the amount of exposure to pollutants(heavy metals): permanent residents (from Enqelab Square to Imam Hossein Square), shopkeepers, vendors andemployees and also students.Calculating the Risk of Developing Cancer and Non-Cancer DiseasesIn this phase, after providing all the required information, the risk of developing cancer and non-cancer diseasesis calculated using following equations. ResultsThe results of analyzing heavy metals in Enqelab Street’s air are presented and discussed. In figure 1, variationsof the mean concentrations of the mentioned metals are provided in the form of a chart. Risk Assessment ResultsIn Enqelab Street, hazard index for chronic and acute exposure is below 1 which shows no adverse effects onnon-cancer disease (figure 2, 3). In addition, the total number of the residents at high risk of developing cancer(types of cancer) by inhaling the heavy metals in their lifetime was estimated to be lower than 24 out of 1 millionpeople. This statistic shows that the conditions have not yet been dangerous. Therefore, through multiplying therate of carcinogenesis by the number of each group, the total number of heavy metal-induced cancers is obtained.In this study, the total number of cancers is three, thus the overall risk is allocated to pollutants including arsenic,cadmium, nickel and lead (figure 4). ConclusionAccording to the presented results, the level of heavy metals in the air of Enqelab Street is not hazardous to thehealth of the residents. Therefore, there is no need to spend enormous expenses in this area. Nevertheless, thehealth of permanent and temporary residents is threatened by chromium and arsenic due to their high rate ofcarcinogenesis. The outcome of these investigations indicates that despite recording few different values in someplaces, the air pollution levels are equal in whole the area, from Enqelab Square to Imam Hossein Square.However, the air pollution level of ValiasCr rossroads is relatively considerable. This difference only hasresulted from high volume of traffic in the crossroads. Unfortunately, traffic of students in this area is so heavythat solving Valiasr Crossroads traffic issues are considered as an important priority.
Journal of Environmental Studies
دانشگاه تهران
1025-8620
39
v.
4
no.
2014
181
192
https://jes.ut.ac.ir/article_36471_4655719095ab00f09a2271bdc97812c0.pdf
dx.doi.org/10.22059/jes.2014.36471
Study of Users Preferences Based on Visual Quality Evaluation:
The Case of Zahedan Mellat Urban Forest Park
Peiman
Golchin
Lecturer, Department of Landscape Design Engineering, University of Sistan &
Balouchestan, Zahedan, Iran
author
Behrooz
Naroei
Lecturer, Department of Landscape Design Engineering, University of Sistan &
Balouchestan, Zahedan, Iran
author
Homa
Irani Behbahani
Assist. Prof., Department of Environmental Design Engineering, University of Tehran
author
text
article
2014
per
IntroductionDue to increase of urbanization and population growth, which cause noticeable changes in the ecologicalstructure of cities, creation of green spaces such as urban forest parks is essential as modulators of urbanenvironment. Urban forest parks are natural or manmade parks that are located within or adjacent to cities andplay an important role in ensuring stability of biodiversity. These parks can also provide environmental,conservational and educational functions as well as being used for leisure times.In the recent years, physical development of urban areas of Zahedan and the vital need of citizens forrecreational spaces caused this forest park to be a popular and suitable place for leisure time. In this research,Zahedan Mellat forest park was selected top rovide reasonable suggestions and strategies by using quality sortmethod and visual evaluation approach. This is to develop anedx tend visual qualities for thesite and alsoidentify landscapes with unique characters.MethodDescriptive – analytical method and site survey was used for this study. First, the required information wascollected through reviewing library resources, articles and internet and then to better understands the case studyand makes site zonation. The site survey was fulfilled by using maps and aerial photos. Parts with moresimilarity in usage, space and activities are considered in one zone. By this, the area is divided into 2 zones asrecreational (zone 1) and auxiliary (zone 2).The quality sort method (Q-method) is used in this research. Q method is a way of extracting and describingsubjective viewpoints. It assumes that subjectivity is structured and it combines qualitative and quantitativeanalyses to provide a systematic and rigorous means for objectively describing human subjectivity. This methodallows respondents to model their viewpoints in response to a sample set of stimuli, which can be statements orimages.The objective of Q method is to systematically describe and compare viewpoints among persons, not todetermine the distribution of viewpoints within a population. Q-method has been applied in many disciplinessuch as political sciences, marketing, psychology, sociology, public policy, marketing, landscape and healthcare.The potential role of Q method in landscape research was recognized early, but has not received only muchattention subsequently. Q method use photographs as a technique to assess scenic values. Subsequent studieshave extended the investigation to assess users' perceptions and classifications of landscape character and alsomake cross cultural comparisons on the perceptions of scenic and heritage landscapes.Through this method, 75 photographs were taken in each of the defined zone. All photos were taken from thestudy site in May 2011 using a digital camera with 50mm wide angle lens. After omitting photos withoutpreferable visual quality or similarity in ways, finally 16 images (8 photos of each zone) were selected forevaluation. Interviewing users were fulfilled on the busiest day of the week (Friday) between 4 until 8 pm.Photos were numbered from 1 to 16 and 100 users were asked to sort them into 5 separate groups labeled verybeautiful, beautiful, normal, ugly and very ugly. Afterwards, the number of each photo was selected by differentusers and their opinions were written down. Selection of users was random.Also users’ age and sex were monotonously distributed. Finally, each photo was evaluated numerically (very beautiful +2, beautiful +1, normal 0 , ugly -1 ,very ugly -2) and the results were analyzed and shown in separatetables and charts. In order to compute achieved points for each photo, the formula below is used:5ii 1N n (3 i) N= Total points for each photon1 =Number of users who choose very beautiful quality photon2 = Number of users who choose beautiful quality photon3 = Number of users who choose normal quality photon4 = Number of users who choose ugly quality photon5 = Number of users who choose very ugly quality photoFinally, the average score of each photo were taken on the chart. Score of each photo indicates thedesirability and quality of the landscape.Discussion and conclusionAfter site survey and interviewing 100 users, collected data was analyzed. Among 100 users, 40 percents weremale and 60 percents were female which 6% were less than 18 years old, 78% between 18 to 34, 14% between34 to 59, and 2% were older than 59 years old. In total, 12 criteria (6 criteria expresses beauty while the other 6reflects non-beauty) were driven from users opinions in order to determine the desirability of landscape with thegreatest influence on visual quality evaluation.Results show that criteria such as presence of mountains and hills, vegetation density and open views to thesurrounding landscape were considered as the top 3 most important criteria effective in landscape visual qualityincrease. While, criteria such as the presence of built human elements, visual disturbance in space and lack ofdiversity in plant species were extracted as the most effective criteria in landscape visual quality abatement.Also, photo number 4 taken in the first zone with the highest average point and criteria such as vegetationdensity was selected as the most beautiful photo while photo number 16 belonging to the second zone with thelowest average point and criteria such as vast presence of human built elements was selected by the users as theleast beautiful photo among other photos. The first zone in comparison with the second one earned the highestaverage point and is qualified as the best zone with the highest visual quality.Overall in this study, the visual quality sort method was used in order to evaluate the landscapecharacteristics of Mellat Forest Park. Having considered all the achieved data, these results were extracted:1. Criteria such as the presence of natural elements like mountains and hills (30 %), dense vegetation andshading (21%) and open view to the surroundings (18%) are considered as the main factors enhancing the visualquality of Mellat Forest Park landscape andc riteria such as the presence of m an-made elements (27%), visualdisturbance (20%), lack of diversity in plants specieasn d lack of vegetation (19%) are the m ajor factors inlandscape visual quality abatement selected by users.2. The results of this study indicate that recreational zone with total average score of 0/1212 is the finest zoneand auxiliary zone with total average scoreo f 0/0575 is rated as the other zone with the highest visual qualityvalues.3. In general, a desirable landscape from user's viewpoint is formed of dense vegetation, natural elements,bright, dark and shadowy spaces and appropriate color combinations, which can provide a good memory or agood sense of place.At the end, in order to promote and improve the research, these further researches are suggested:1. Necessity of providing a comprehensive plan for Zahedan City and considering its impact on Park'sdevelopment prospects2. Study and research to assign the appropriate use to the natural - cultural fields of this Park3. Quality promotion and improvement based on users needs, environmental standards and aesthetic factors.4. Considering planting design principals and selection of suitable vegetation compatible to the regionsclimate.
Journal of Environmental Studies
دانشگاه تهران
1025-8620
39
v.
4
no.
2014
193
203
https://jes.ut.ac.ir/article_36472_f49f9081b5a10e5b053035038b1d013a.pdf
dx.doi.org/10.22059/jes.2014.36472
English Abstracts
text
article
2014
per
Journal of Environmental Studies
دانشگاه تهران
1025-8620
39
v.
4
no.
2014
1
40
https://jes.ut.ac.ir/article_36473_d41d8cd98f00b204e9800998ecf8427e.pdf
dx.doi.org/10.22059/jes.2014.36473